TAILIEUCHUNG - Báo cáo khoa học: "Text Segmentation Using Reiteration and Collocation"

A method is presented for segmenting text into subtopic areas. The proportion of related pairwise words is calculated between adjacent windows of text to determine their lexical similarity. The lexical cohesion relations of reiteration and collocation are used to identify related words. These relations are automatically located using a combination of three linguistic features: word repetition, collocation and relation weights. This method is shown to successfully detect known subject changes in text and corresponds well to the segmentations placed by test subjects. . | Text Segmentation Using Reiteration and Collocation Amanda c. Jobbins Department of Computing Nottingham Trent University Nottingham NG1 4BU UK ajobbins@ Lindsay J. Evett Department of Computing Nottingham Trent University Nottingham NG1 4BU UK lje@ Abstract A method is presented for segmenting text into subtopic areas. The proportion of related pairwise words is calculated between adjacent windows of text to determine their lexical similarity. The lexical cohesion relations of reiteration and collocation are used to identify related words. These relations are automatically located using a combination of three linguistic features word repetition collocation and relation weights. This method is shown to successfully detect known subject changes in text and corresponds well to the segmentations placed by test subjects. Introduction Many examples of heterogeneous data can be found in daily life. The Wall Street Journal archives for example consist of a series of articles about different subject areas. Segmenting such data into distinct topics is useful for information retrieval where only those segments relevant to a user s query can be retrieved. Text segmentation could also be used as a pre-processing step in automatic summarisation. Each segment could be summarised individually and then combined to provide an abstract for a document. Previous work on text segmentation has used term matching to identify clusters of related text. Salton and Buckley 1992 and later Hearst 1994 extracted related text portions by matching high frequency terms. Yaari 1997 segmented text into a hierarchical structure identifying sub-segments of larger segments. Ponte and Croft 1997 used word co-occurrences to expand the number of terms for matching. Reynar 1994 compared all words across a text rather than the more usual nearest neighbours. A problem with using word repetition is that inappropriate matches can be made because of the lack of contextual information .

• Báo cáo khoa học
• Text Segmentation
• Reiteration and Collocation
• Amanda C
• Jobbins
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Text Segmentation by Language Using Minimum Description Length
• Hiroshi Yamaguchi
• Kumiko
• Tanaka Ishii
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• LDA Based Fisher Kernel
• Qi Sun
• Runxin Li
• Dingsheng Luo and Xihong Wu
• Unsupervised Segmentation
• Chinese Text
• Use of Branching Entropy
• A Statistical Model
• Domain Independent
• Multiple Surface Linguistic Cues
• Long Papers
• Optimal Multi Paragraph Text Segmentation
• Dynamic Programming
• Oskari Heinonen
• Cohesion and Collocation
• Using Context Vectors
• BASED TEXT SEGMENTATION
• SIMILARITY BETWEEN WORDS
• Hideki Kozima
• MULTI PARAGRAPH SEGMENTATION
• EXPOSITORY TEXT
• Marti A
• Hearst
• Linear Text Segmentation
• Dynamic Programming Algorithm
• Fragkou Pavlina
• scientific reports
• model language
• process natural language
• giáo dục
• đào tạo
• cao đẳng
• đại học
• Linuxs solution
• LXR can help
• Objdump can disassemble
• Use a text editor
• Role of segmentation
• A race cure case study
• International Journal of Computer Science & Communication Networks
• Cluster based hierarchical approach
• The recognition of bengali handwritten character
• Bengali handwritten character
• Bengali text segmentation